From 9c6c1e71f8490ecc247f4f694cd8d45626cb6666 Mon Sep 17 00:00:00 2001 From: Abubakar Abid Date: Tue, 3 Aug 2021 14:49:47 -0500 Subject: [PATCH] Update README.md (#116) Fix typo --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index f88ef942a..51dcc0d08 100644 --- a/README.md +++ b/README.md @@ -76,7 +76,7 @@ new_col: mk.ImageColumn = dp["image"][10:20] **`DataPanel` supports `map`, `update` and `filter` operations.** When training and evaluating our models, we often perform operations on each example in our dataset (*e.g.* compute a model's prediction on each example, tokenize each sentence, compute a model's embedding for each example) and store them . The `DataPanel` makes it easy to perform these operations and produce new columns (via `DataPanel.map`), store the columns alongside the original data (via `DataPanel.update`), and extract an important subset of the datset (via `DataPanel.filter`). Under the hood, dataloading is multiprocessed so that costly I/O doesn't bottleneck our computation. Consider the example below where we use update a `DataPanel` with two new columns holding model predictions and probabilities. ```python # A simple evaluation loop using Meerkat -dp: DataPane = ... # get DataPane +dp: DataPanel = ... # get DataPanel model: nn.Module = ... # get the model model.to(0).eval() # prepare the model for evaluation